Real-time underwater image resolution enhancement using super-resolution with deep convolutional neural networks
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Real‑time underwater image resolution enhancement using super‑resolution with deep convolutional neural networks Mohammad Kazem Moghimi1 · Farahnaz Mohanna1 Received: 29 April 2020 / Accepted: 21 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this paper, a two-step image enhancement is presented. In the first step, color correction and underwater image quality enhancement are conducted if there are artifacts such as darkening, hazing and fogging. In the second step, the image resolution optimized in the previous step is enhanced using the convolutional neural network (CNN) with deep learning capability. The main reason behind the adoption of this two-step technique, which includes image quality enhancement and super-resolution, is the need for a robust strategy to visually improve underwater images at different depths and under diverse artifact conditions. The effectiveness and robustness of the real-time algorithm are satisfactory for various underwater images under different conditions, and several experiments have been undertaken for the two datasets of images. In both stages and for each of image datasets, the mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity (SSIM) evaluation measures were fulfilled. In addition, the low computational complexity and suitable outputs were obtained for different artifacts that represented divergent depths of water to achieve a real-time system. The super-resolution in the proposed structure for medium layers can offer a proper response. For this reason, time is also one of the major factors reported in the research. Applying this model to underwater imagery systems will yield more accurate and detailed information. Keywords Underwater images · Real-time · Quality enhancement · Color correction · Super-resolution · Convolutional neural network
1 Introduction Underwater imaging plays an effective role in ocean exploration but often suffer from severe quality degradation due to artifacts and light absorption. Today, most of the underwater vehicles used for underwater exploration are usually equipped with optical cameras to capture visual data of underwater objects, shipwrecks, coral reefs, pipelines and telecommunications lines in the seas and oceans, etc., [1, 2]. For example, for regular inspections of oil and gas pipes in the seas and oceans, ROVs equipped with these cameras are recruited with the human operators onshore analyzing images transmitted by these devices [3–5]. However, the color images captured with these cameras, due to the * Farahnaz Mohanna [email protected] Mohammad Kazem Moghimi [email protected] 1
Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran
physical properties of the aquatic environment, have nonreal-time manner, poor visual quality, opacity or luminosity with a poor field of view. The light is exponentially attenuated when travelling through the water, causing these images to have low contrast
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